ECCDN-Net: A deep learning-based technique for efficient organic and recyclable waste classification.

Efficient waste management is essential to minimizing environmental harm as well as encouraging sustainable progress. The escalating volume and sophistication of waste present significant challenges, prompting innovative methods for effective waste categorization and management. Deep learning models have become highly intriguing tools for automating trash categorization activities, providing effective ways to optimize processes for handling waste. Ourwork presents a novel deep learning method for trash classification, with the goal to improve the accuracy, also efficiency of garbage image categorization. We examined the effectiveness of several pre-trained models, such as InceptionV2, Densenet201, MobileNet v2, and Resnet18, using objective evaluation and cross-validation. We proposed an Eco Cycle Classifier Deep Neural Network (ECCDN-Net) model that is particularly built for the categorization of waste images. ECCDN-Net utilizes the advantageous qualities of Densenet201 and Resnet18 by merging their capacities to extract features, enhanced with auxiliary outputs to optimize the classification procedure. The set of imagesused in this study comprises 24,705 images that are divided into two distinct classes: Organic and Recyclable. The set allows extensive evaluation and training of deep learning models for waste classification of images tasks. Our research demonstrates that the ECCDN-Net model classifies waste images with 96.10% accuracy, outperforming other pre-trained models. Resnet18 had 92.68% accuracy, MobileNet v2 93.27%, Inception v3 94.77%, and Densenet201, a significant improvement, 95.98%. ECCDN-Net outperformed these models in waste image categorization with 96.10% accuracy. We ensure the reliability and generalizability of our methods throughout the dataset by integrating and cross-validating deep learning models. The current work introduces an innovative deep learning-based approach that has promising potential for waste categorization and management strategies.